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arXiv:2610.28567v1 Announce Type: cross Abstract: Text-to-image personalization aims to equip pretrained diffusion models with the ability to learn user-specific concepts from only a few reference im...
arXiv:2610.01331v1 Announce Type: new Abstract: Continual personalization of text-to-image diffusion models requires sequentially acquiring new concepts while retaining previously learned ones. Howev...
arXiv:2511.22245v2 Announce Type: replace Abstract: Personalizing text-to-image diffusion models extends pretrained models to represent novel user-specific concepts from only a few reference images....
SeqLoRA introduces a bilevel optimization framework for continual learning of text‑to‑image diffusion models, jointly optimizing LoRA factors while enforcing orthogonality between new and existing concept bases. The method achieves monotone descent, convergence guarantees, and a high‑probability bound on catastrophic forgetting that depends on residual interference energy. Experiments on Stable Diffusion with up to 101 concepts demonstrate improved identity preservation, minimal cross‑concept leakage, no need for a fusion step, and scalability beyond memory limits of fusion‑based approaches.
arXiv:2605.19750v2 Announce Type: replace Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation, yet their personalization capabilit...
arXiv:2609.37537v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive re...